Predictive analytics in determining key performance indicators

ABSTRACT

Disclosed are a system, computer readable medium and method for predicting key performance indicators. The method includes receiving one or more data pairs, the one or more data pairs indicating a performance parameter and reason indicator associated with the performance parameter, deriving a formulaic relationship, utilizing a regression formula, between the reason indicator and the performance parameter, predicting at least one key performance indicator (KPI), utilizing a regression formula, for each of the one or more data pairs, associating a cost with each of the one or more data pairs, and varying a parameter based on the KPI and the associated cost.

FIELD

Embodiments relate to utilizing regression formulas to predict keyperformance indicators.

BACKGROUND

Large quantities of data are typically collected in many types ofmanufacturing environments. For example, data is collected for systemsregarding product quality, system performance, system availability andcosts. However, due to the nature of this data (e.g., quantity) systemshave not been able to efficiently leverage this data in order tooptimally set manufacturing equipment parameters in manufacturingenvironments or to predict the effects on the manufacturing environmentupon setting manufacturing equipment parameters.

SUMMARY

One embodiment includes a method for predicting key performanceindicators. The method includes receiving one or more data pairs, theone or more data pairs indicating a performance parameter and reasonindicator associated with the performance parameter, deriving aformulaic relationship, utilizing a regression formula, between thereason indicator and the performance parameter, predicting at least onekey performance indicator (KPI), utilizing a regression formula, foreach of the one or more data pairs, associating a cost with each of theone or more data pairs, and varying a parameter based on the KPI and theassociated cost.

Another embodiment includes a high-performance analytic appliance(HANA). The HANA includes at least one processor, and at least onememory storing code segments. When the code segments are executed by theprocessor, the processor receives one or more data pairs, the one ormore data pairs indicating a performance parameter and reason indicatorassociated with the performance parameter, derives a formulaicrelationship, utilizing a regression formula, between the reasonindicator and the performance parameter, predicts at least one keyperformance indicator (KPI), utilizing a regression formula, for each ofthe one or more data pairs, associates a cost with each of the one ormore data pairs, and varies a parameter based on the KPI and theassociated cost.

BRIEF DESCRIPTION OF THE DRAWINGS

Example embodiments will become more fully understood from the detaileddescription given herein below and the accompanying drawings, whereinlike elements are represented by like reference numerals, which aregiven by way of illustration only and thus are not limiting of theexample embodiments and wherein:

FIG. 1 illustrates a system using performance indicators and predictiveanalysis according to at least one example embodiment.

FIG. 2 illustrates an in-memory computing module according to at leastone example embodiment.

FIG. 3 illustrates a manufacturing system using the system of FIG. 1according to an example embodiment.

FIG. 4 illustrates a method according to at least one exampleembodiment.

It should be noted that these Figures are intended to illustrate thegeneral characteristics of methods, structure and/or materials utilizedin certain example embodiments and to supplement the written descriptionprovided below. These drawings are not, however, to scale and may notprecisely reflect the precise structural or performance characteristicsof any given embodiment, and should not be interpreted as defining orlimiting the range of values or properties encompassed by exampleembodiments. For example, the relative thicknesses and positioning ofmolecules, layers, regions and/or structural elements may be reduced orexaggerated for clarity. The use of similar or identical referencenumbers in the various drawings is intended to indicate the presence ofa similar or identical element or feature.

DETAILED DESCRIPTION OF THE EMBODIMENTS

While example embodiments may include various modifications andalternative forms, embodiments thereof are shown by way of example inthe drawings and will herein be described in detail. It should beunderstood, however, that there is no intent to limit exampleembodiments to the particular forms disclosed, but on the contrary,example embodiments are to cover all modifications, equivalents, andalternatives falling within the scope of the claims. Like numbers referto like elements throughout the description of the figures.

Brief Discussion of Equations

A common form of a linear equation in the two variables x and y isy=mx+b where m and b designate constants. The origin of the name“linear” comes from the fact that the set of solutions of such anequation forms a straight line in the plane. In this particularequation, the constant m determines the slope or gradient of that lineand the constant term b determines the point at which the line crossesthe y-axis, otherwise known as the y-intercept.

Regression is a statistical technique used to explain or predict thebehavior of a dependent variable. A regression model may predict a valueY based on a function of X and β such that Y≈(X, β) Generally, aregression equation takes the form of Y=a+bx+e, where Y is the dependentvariable that the equation tries to predict, X is the independentvariable that is being used to predict Y, a is the Y-intercept of theline, and e is a value called the regression residual. In a generalmultiple regression model, there are p independent variables:

y=β ₁ x _(i1)+β₂ x _(i2)+ . . . +β_(p) x _(ip)+ε_(i)  (1)

where,

-   -   i=1, n,    -   x_(ij) is the i^(th) observation on the j^(th) independent        variable, and    -   the first independent variable takes the value 1 for all i.

Equation 1 may be rewritten as:

y _(i) =x _(i) ^(T)β+ε_(i)  (2)

where,

-   -   i=1, . . . , n, and    -   T is time in that the observations (x) may be time bound.

In linear algebraic form, equation 2 may be rewritten utilizing thefollowing equations:

$\begin{matrix}{y = \begin{pmatrix}y_{1} \\y_{2} \\\vdots \\y_{n}\end{pmatrix}} & (3) \\{X = {\begin{pmatrix}x_{1}^{T} \\x_{2}^{T} \\\vdots \\x_{n}^{T}\end{pmatrix} = \begin{pmatrix}x_{11} & \ldots & x_{1\; p} \\x_{21} & \ldots & x_{2\; p} \\\vdots & \ddots & \vdots \\x_{n\; 1} & \ldots & x_{np}\end{pmatrix}}} & (4) \\{\beta = \begin{pmatrix}\beta_{1} \\\beta_{2} \\\vdots \\\beta_{p}\end{pmatrix}} & (5) \\{ɛ = \begin{pmatrix}ɛ_{1} \\ɛ_{2} \\\vdots \\ɛ_{n}\end{pmatrix}} & (6)\end{matrix}$

Thus, equation 2 may be rewritten as:

$\begin{matrix}{\begin{pmatrix}y_{1} \\y_{2} \\\vdots \\y_{n}\end{pmatrix} = {{\begin{pmatrix}x_{11} & \ldots & x_{1\; p} \\x_{21} & \ldots & x_{2\; p} \\\vdots & \ddots & \vdots \\x_{n\; 1} & \ldots & x_{np}\end{pmatrix}\begin{pmatrix}\beta_{1} \\\beta_{2} \\\vdots \\\beta_{p}\end{pmatrix}} + \begin{pmatrix}ɛ_{1} \\ɛ_{2} \\\vdots \\ɛ_{n}\end{pmatrix}}} & (7)\end{matrix}$

Example embodiments may acquire and/or determine the independentvariables (e.g., x and β) in the above equations and solve at least oneof equations 1-7 for y or y_(i) where y or y_(i) is a key performanceindicator (KPI).

Discussion of Example Embodiments

FIG. 1 illustrates a system using performance indicators and predictiveanalysis according to at least one example embodiment. As shown in FIG.1, the system 100 includes equipment 105, an equipment controller 110 adisplay 115, and a high-performance analytic appliance (HANA) 120. TheHANA 120 may have an associated in-memory computing module 125.

For example, the equipment 105 may be manufacturing equipment (e.g., alathe, a robot, a computer numerical control (CNC) machine, bakingequipment and the like). The equipment controller 110 may provide allnecessary control instructions for operating the equipment 105. Forexample the equipment controller may include memory, a processor, a bus,switches, interfaces and other circuits and devices (not shown) tocontrol the operation of the equipment 105. Display 115 may be, forexample, a LCD display configured to provide a user interface with anoperator of the equipment 105.

The HANA 120 (e.g., SAP™ HANA) may be a data warehouse appliance forprocessing high volumes of operational and transactional data inreal-time. HANA may use in-memory analytics, an approach that queriesdata stored in random access memory (RAM) instead of on hard disk orflash storage.

The HANA 120 may use a replication server (e.g., SAP Sybase replicationserver) to copy and synchronize data from a data warehouse (e.g., HANA120 being in the form of an in-memory database) in real-time. By runningin parallel to the source application and/or data warehouse, the HANA120 may be configured to allow users to query large volumes of data inreal-time without having to wait for scheduled reports to run. The HANA120 may be configured to support known standards such as structuredquery language (SQL). The HANA 120 may include a programming component(not shown) configured to allow creation (and editing) of and runcustomized applications on top of the HANA 120.

HANA 120 may be configured to store information about the operation ofthe equipment 105. For example, HANA 120 may store real-time andhistorical information about the equipment 105. The real-time andhistorical information may include, but is not limited to, operatingspeed, pieces per hour, quality statistics, temperatures, up-times,down-times and maintenance information (e.g., time to next service,component runtimes and the like).

The in-memory computing module 125 may be configured to receive datafrom and provide data to the equipment controller 110. The equipmentcontroller 110 may use the provided data to, for example, set parameters(e.g., a target speed) of the equipment 105.

FIG. 2 illustrates the in-memory computing module 125 according to atleast one example embodiment. As shown in FIG. 2, the in-memorycomputing module 125 includes at least one processing unit 205, at leastone memory 210 and a prediction module 215. The at least one processingunit 205, the at least one memory 210 and the prediction module 215 arecommunicatively coupled via bus 220. The in-memory computing module 125may be, for example, a software implementation or an integrated circuit(e.g., application-specific integrated circuit).

In the example of FIG. 2, the in-memory computing module 125 may be atleast one computing device and should be understood to representvirtually any computing device configured to perform the methodsdescribed herein. As such, the in-memory computing module 125 may beunderstood to include various standard components which may be utilizedto implement the techniques described herein, or different or futureversions thereof. By way of example, the in-memory computing module 125is illustrated as including the at least one processing unit 205, aswell as at least one memory 210 (e.g., a computer readable storagemedium).

Thus, as may be appreciated, the at least one processing unit 205 may beutilized to execute instructions stored on the at least one memory 210,so as to thereby implement the various features and functions describedherein, or additional or alternative features and functions. Of course,the at least one processing unit 205 and the at least one memory 210 maybe utilized for various other purposes. In particular, it may beappreciated that the at least one memory 210 may be understood torepresent an example of various types of memory and related hardware andsoftware which might be used to implement any one of the modulesdescribed herein.

The at least one memory 210 may store an algorithm library. In HANA, thealgorithm library may be known as a predictive algorithm library (PAL).The PAL may store algorithms associated with clustering, classification,association, and the like. For example, the PAL may storeclassifications algorithms including linear regression algorithms.Therefore, the PAL may store algorithms based on equations 1-7 describedabove.

As described above, HANA 120 may be configured to store information. Theinformation may be stored in the at least one memory 210. Theinformation may include data associated with a machine (e.g., machine105), non-machine systems (e.g., inventory) and/or multi-elementsystems. For example, the information may include data associated withavailability. Data associated with availability may include unscheduleddowntime, extended break time, power loss, missing parts (maintenance orinventory), inconsistent timing, and the like. For example, theinformation may include data associated with performance. Dataassociated with performance may include calibration, quality,overloading, speed (increases or decreases), operator error, and thelike.

For example, the information may include data associated with quality.Data associated with quality may include availability of parts, kanbandelay, missing operators, rejected quantities, start-up instructions notfollowed, and the like. For example, the information may include dataassociated with efficiency or effectiveness. Data associated withefficiency or effectiveness may include tooling loss, toolingmaintenance, waiting on system start-up events, system operationvariability (e.g., temperature variation or spikes), and the like.Availability, performance, quality and/or efficiency (or effectiveness)may be called key performance indicators (KPI). KPI may include othermeasurable or determinable equipment and/or system information.

Machine (or system) efficiency (or effectiveness) may be determinedbased on other KPI. For example, efficiency (or effectiveness) may bedetermined based on losses (or reductions) in KPI. The losses may be,for example, availability losses, performance losses and/or qualitylosses. Availability losses may include set-up losses (e.g., poorplanning or scheduling, insufficient operator skills, poor start-upcontrols, missing parts, poor tooling, and/or the like) and/or breakdownlosses (e.g., lack of maintenance, operator inattentiveness, poordesign, poor training, material failures, and/or the like).

Performance losses may include minor stoppages (as opposed tobreakdowns) (e.g., material not available, operator error/absence,jams/misfeeds/overloads, changeover, and/or the like) and/or speedlosses (e.g., incorrect settings, deliberate reduction, poor training,unclear design specifications, and/or the like). Quality losses mayinclude start-up losses (e.g., poor machine change over, inconsistentmaterials, start-up checklist incorrect/unavailable, waiting on machineparameter stabilization, machine adjustment, and/or the like) and/orin-process losses (e.g., parameter (temperature, pressure) changes,inconsistent materials, process not followed, calibration, operatorerror, quantity rejections, and/or the like). Each of the aforementionedexamples of losses may be given an associated reason indicator and mayinclude an associated value related to, for example, difficulty tocontrol or system impact. For example, the more difficult to control theloss and/or the more impactful the loss, the higher the value may be.

The prediction module 215 may be configured to predict a future KPIbased on varying machine or system parameters and/or varying associateddata (e.g., availability data, performance data quality data and/orefficiency (or effectiveness) data). The varying data may be dependentor independent data. For example, varying parts availability may resultin a varying availability. Other dependent/independent relationships maybe readily apparent in the above discussion of stored information.

FIG. 3 illustrates a manufacturing system using the system of FIG. 1according to an example embodiment. As shown in FIG. 3, the systemincludes HANA 120, equipment controllers 110-1 to 110-n (described abovewith regard to equipment controller 110), and other data sources 305-1to 305-n. The other data sources 305-1 to 305-n may include elements ofa manufacturing system. For example, the other data sources 305-1 to305-n may include inventory data sources (e.g., kanban bins and/orinventory information systems), quality control input systems (e.g.,cameras and/or customer return data entry systems), operator informationentry systems (e.g., data entry for failure codes) and/or the like. HANA120, equipment controllers 110-1 to 110-n, and other data sources 305-1to 305-n may be communicatively coupled via, for example, a local areanetwork (LAN).

FIG. 4 illustrates a method according to at least one exampleembodiment. The method steps described with regard to FIG. 4 may beexecuted as software code stored in a memory (e.g., at least one memory210) associated with a HANA system (e.g., as shown in FIGS. 1 and 3) andexecuted by at least one processor (e.g., processor 205) associated withthe HANA system. However, alternative embodiments are contemplated suchas a HANA embodied as a special purpose processor.

For example, the method steps may be performed by anapplication-specific integrated circuit, or ASIC. For example, the ASICmay be configured as the HANA 120 and/or the in-memory computing module125. Although the steps described below are described as being executedby a processor, the steps are not necessarily executed by a sameprocessor. In other words, at least one processor may execute the stepsdescribed below with regard to FIG. 4.

As shown in FIG. 4, in step S405, a processor receives one or more datapairs. The one or more data pairs may be received from an in-memorycomputing module (e.g., in-memory computing module 125). The in-memorycomputing module may be a HANA (e.g., HANA 120). The one or more datapairs may indicate a performance parameter and a reason indicatorassociated with the performance parameter. For example, as discussedabove the performance parameter may be associated with availability.Therefore, the performance parameter may include an indicator (e.g.,up-time or down-time value) indicating overall availability. Further, asdiscussed above, availability may be further defined in terms ofavailability losses which may further be defined in terms of set-up(losses) and breakdown (losses). The performance parameter may bedefined with respect to sub-terms. For example, the performanceparameter may be availability losses, performance losses, quality lossesand/or the like. For example, the performance parameter may be set-up(losses), breakdown (losses), minor stoppages, speed losses, start-up(quality) losses, in-process (quality) losses, and/or the like.Therefore, the performance parameter may be any variable or measurable(manufacturing) parameter that may have an associated reason for thevariation.

The reason indicator may include a code associated with the performanceparameter. For example, the reason indicator may include a code, forexample a reason code, associated with breakdown, availability lossesand/or availability. The associated reason code may be associated with abreakdown (availability loss and/or availability) reason (e.g., lack ofmaintenance, operator inattentiveness, poor design, poor training,material failures, and/or the like). For example, there may be a valuerange for reason codes (e.g., 0 to 10 or 0% to 100%) such that a firstreason code value range (for breakdown, availability losses and/oravailability) is associated with lack of maintenance and so forth forthe remaining reason codes. The value range may indicate a severityassociated with the reason code. Similarly, the reason indicator may bebased on performance, quality, efficiency (or effectiveness) and thelike based performance parameters.

In step S410, the processor utilizes a regression formula to derive aformulaic relationship between the reason indicator and the performanceparameter. For example, utilizing equation 7 above, independentvariables (e.g., x and β) may be expressed in terms of each other. Forexample, X (from equation 4) may be one or more reason indicators. Forexample, x₁ may be material failures associated with availability and x₂may be kanban delay associated with quality. Each of x₁ and x₂ may rangebetween 0% and 100%. For each of the combinations, a corresponding β(from equation 5) may be derived utilizing a first regression formula.

Deriving formulaic relationship between the reason indicator and theperformance parameter may include selecting the regression formula from,for example, the HANA predictive algorithm library (PAL). The reasonindicator and the performance parameter may be input as dependentvariables in the selected regression formula. The formulaic relationshipmay be generated as linear dependencies between the reason indicator andthe performance parameter. For example, HANA PAL may include a functionthat when called may return the formulaic relationship as, for example adata table. The HANA PAL function may include, as input, at least onetable with input (e.g., historical, configuration, target, and/or thelike) data.

In step S415, the processor utilizes a regression formula to determineand/or predict one or more key performance indicator(s) (KPI) for one ormore of a system associated with each of the one or more data pairs orfor each of the one or more data pairs. For example, equation 7 may beutilized as the regression formula. In the previous step, X fromequation 4 and the corresponding β from equation 5 may be derived whereX and β may be a formulaic relationship between the reason indicator andthe performance parameter. Substituting X from equation 4 and thecorresponding β from equation 5 into equation 7 and solving for theresult y, where y is KPI, may be the determined and/or predicted one ormore key performance indicator(s) (KPI).

In step S420, the processor associates a cost with each of the one ormore data pairs. For example, cost for each reason code may be derivedfrom an activity key associated with the reason code and/or datacollection elements. This activity may be mapped such that cost per unitof the activity may be determined. For example, a per unit (e.g., each1%) cost for each reason code may be stored in a database or XML file inthe at least on memory 210. The at least one processor 205 may the querythe database or XML file and associate the result with the one or moredata pairs. The query result may be scaled by the value (e.g., 0 to 10or 0% to 100%) associated with the reason code. The cost for a pluralityof data pairs for an associated plurality of reason indicators may bedetermined where parameters for a plurality of elements (e.g., as shownin FIG. 3) are affected.

In step S425, the processor varies a parameter based on the KPI and theassociated cost. For example, varying the parameter may affect one ormore of the performance parameters. As described above, HANA 120 may beconfigured to store information. The information may include dataassociated with a machine (e.g., machine 105), non-machine systems(e.g., inventory) and/or multi-element systems. For example, theinformation may include data associated with availability, dataassociated with quality, data associated with efficiency oreffectiveness and/or the like.

As discussed above, availability, quality, efficiency or effectivenessand/or the like may be KPI. Therefore, the processor may vary aparameter that the aforementioned data is dependent on. For example,performance may be dependent on speed. Therefore, the processor may varya parameter that affects speed. For example, quality may be dependent onkanban delay. Therefore, the processor may vary a parameter that affectskanban delay. According to example embodiments, the processor may varythe parameter in a simulated environment in order to develop a bestpractices configuration for a system that the simulated environment isbased on.

Some of the above example embodiments are described as processes ormethods depicted as flowcharts. Although the flowcharts describe theoperations as sequential processes, many of the operations may beperformed in parallel, concurrently or simultaneously. In addition, theorder of operations may be re-arranged. The processes may be terminatedwhen their operations are completed, but may also have additional stepsnot included in the figure. The processes may correspond to methods,functions, procedures, subroutines, subprograms, etc.

Methods discussed above, some of which are illustrated by the flowcharts, may be implemented by hardware, software, firmware, middleware,microcode, hardware description languages, or any combination thereof.When implemented in software, firmware, middleware or microcode, theprogram code or code segments to perform the necessary tasks may bestored in a machine or computer readable medium such as a storagemedium. A processor(s) may perform the necessary tasks.

Specific structural and functional details disclosed herein are merelyrepresentative for purposes of describing example embodiments. Exampleembodiments, however, be embodied in many alternate forms and should notbe construed as limited to only the embodiments set forth herein.

It will be understood that, although the terms first, second, etc. maybe used herein to describe various elements, these elements should notbe limited by these terms. These terms are only used to distinguish oneelement from another. For example, a first element could be termed asecond element, and, similarly, a second element could be termed a firstelement, without departing from the scope of example embodiments. Asused herein, the term “and/or” includes any and all combinations of oneor more of the associated listed items.

It will be understood that when an element is referred to as being“connected” or “coupled” to another element, it can be directlyconnected or coupled to the other element or intervening elements may bepresent. In contrast, when an element is referred to as being “directlyconnected” or “directly coupled” to another element, there are nointervening elements present. Other words used to describe therelationship between elements should be interpreted in a like fashion(e.g., “between” versus “directly between,” “adjacent” versus “directlyadjacent,” etc.).

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of exampleembodiments. As used herein, the singular forms “a,” “an” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. It will be further understood that the terms“comprises,” “comprising,” “includes” and/or “including,” when usedherein, specify the presence of stated features, integers, steps,operations, elements and/or components, but do not preclude the presenceor addition of one or more other features, integers, steps, operations,elements, components and/or groups thereof.

It should also be noted that in some alternative implementations, thefunctions/acts noted may occur out of the order noted in the figures.For example, two figures shown in succession may in fact be executedconcurrently or may sometimes be executed in the reverse order,depending upon the functionality/acts involved.

Unless otherwise defined, all terms (including technical and scientificterms) used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which example embodiments belong. Itwill be further understood that terms, e.g., those defined in commonlyused dictionaries, should be interpreted as having a meaning that isconsistent with their meaning in the context of the relevant art andwill not be interpreted in an idealized or overly formal sense unlessexpressly so defined herein.

Portions of the above example embodiments and corresponding detaileddescription are presented in terms of software, or algorithms andsymbolic representations of operation on data bits within a computermemory. These descriptions and representations are the ones by whichthose of ordinary skill in the art effectively convey the substance oftheir work to others of ordinary skill in the art. An algorithm, as theterm is used here, and as it is used generally, is conceived to be aself-consistent sequence of steps leading to a desired result. The stepsare those requiring physical manipulations of physical quantities.Usually, though not necessarily, these quantities take the form ofoptical, electrical, or magnetic signals capable of being stored,transferred, combined, compared, and otherwise manipulated. It hasproven convenient at times, principally for reasons of common usage, torefer to these signals as bits, values, elements, symbols, characters,terms, numbers, or the like.

In the above illustrative embodiments, reference to acts and symbolicrepresentations of operations (e.g., in the form of flowcharts) that maybe implemented as program modules or functional processes includeroutines, programs, objects, components, data structures, etc., thatperform particular tasks or implement particular abstract data types andmay be described and/or implemented using existing hardware at existingstructural elements. Such existing hardware may include one or moreCentral Processing Units (CPUs), digital signal processors (DSPs),application-specific-integrated-circuits, field programmable gate arrays(FPGAs) computers or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise, or as is apparent from the discussion,terms such as “processing” or “computing” or “calculating” or“determining” of “displaying” or the like, refer to the action andprocesses of a computer system, or similar electronic computing device,that manipulates and transforms data represented as physical, electronicquantities within the computer system's registers and memories intoother data similarly represented as physical quantities within thecomputer system memories or registers or other such information storage,transmission or display devices.

Note also that the software implemented aspects of the exampleembodiments are typically encoded on some form of program storage mediumor implemented over some type of transmission medium. The programstorage medium may be magnetic (e.g., a floppy disk or a hard drive) oroptical (e.g., a compact disk read only memory, or “CD ROM”), and may beread only or random access. Similarly, the transmission medium may betwisted wire pairs, coaxial cable, optical fiber, or some other suitabletransmission medium known to the art. The example embodiments notlimited by these aspects of any given implementation.

Lastly, it should also be noted that whilst the accompanying claims setout particular combinations of features described herein, the scope ofthe present disclosure is not limited to the particular combinationshereafter claimed, but instead extends to encompass any combination offeatures or embodiments herein disclosed irrespective of whether or notthat particular combination has been specifically enumerated in theaccompanying claims at this time.

What is claimed is:
 1. A method, comprising: receiving one or more datapairs, the one or more data pairs indicating a performance parameter andreason indicator associated with the performance parameter; deriving aformulaic relationship, utilizing a regression formula, between thereason indicator and the performance parameter; predicting at least onekey performance indicator (KPI), utilizing a regression formula, foreach of the one or more data pairs; associating a cost with each of theone or more data pairs; and varying a parameter based on the KPI and theassociated cost.
 2. The method of claim 1, wherein the one or more datapairs is received from an in-memory computing module; and the in-memorycomputing module is associated with a high performance analyticappliance (HANA).
 3. The method of claim 1, wherein the performanceparameter includes data associated with performance of at least one of amanufacturing machine, an element of a manufacturing system and amanufacturing system.
 4. The method of claim 3, wherein element of amanufacturing system includes at least one of inventory data sources,quality control systems, and operator information entry systems.
 5. Themethod of claim 1, wherein the reason indicator is a code associatedwith the performance parameter.
 6. The method of claim 1, wherein thereason indicator includes value range indicating a severity associatedwith the reason code.
 7. The method of claim 1, wherein deriving aformulaic relationship, utilizing a regression formula, between thereason indicator and the performance parameter includes, selecting theregression formula, inputting the reason indicator and the performanceparameter as independent variables in the regression formula, andgenerating the formulaic relationship as linear dependencies.
 8. Themethod of claim 1, wherein predicting at least one key performanceindicator (KPI), utilizing a regression formula includes, selecting afirst regression formula, inputting the reason indicator and theperformance parameter as independent variables in the first regressionformula, generating linear dependencies between the reason indicator andthe performance parameter, selecting a second regression formula,inputting the linear dependent reason indicator and performanceparameter as independent variables in the second regression formula, andgenerating the at least one KPI as linear dependent variables of thesecond regression formula.
 9. The method of claim 1, wherein the costassociated with each of the one or more data pairs is determined basedon a cost per unit of an activity associated with the reason indicator.10. A high-performance analytic appliance (HANA), comprising: at leastone processor, and at least one memory storing code segments that whenexecuted by the processor cause the processor to, receive one or moredata pairs, the one or more data pairs indicating a performanceparameter and reason indicator associated with the performanceparameter; derive a formulaic relationship, utilizing a regressionformula, between the reason indicator and the performance parameter;predict at least one key performance indicator (KPI), utilizing aregression formula, for each of the one or more data pairs; associate acost with each of the one or more data pairs; and vary a parameter basedon the KPI and the associated cost.
 11. The HANA of claim 10, whereinthe performance parameter includes data associated with performance ofat least one of a manufacturing machine, an element of a manufacturingsystem and a manufacturing system.
 12. The HANA of claim 11, wherein theelement of a manufacturing system includes at least one of inventorydata sources, quality control systems, and operator information entrysystems.
 13. The HANA of claim 10, wherein the reason indicator is acode associated with the performance parameter.
 14. The HANA of claim10, wherein the reason indicator includes value range indicating aseverity associated with the reason code.
 15. The HANA of claim 10,wherein deriving a formulaic relationship, utilizing a regressionformula, between the reason indicator and the performance parameterfurther cause the processor to, select the regression formula, input thereason indicator and the performance parameter as independent variablesin the regression formula, and generate the formulaic relationship aslinear dependencies.
 16. The HANA of claim 10, wherein predicting atleast one key performance indicator (KPI), utilizing a regressionformula further causes the processor to, select a first regressionformula, input the reason indicator and the performance parameter asindependent variables in the first regression formula, generate lineardependencies between the reason indicator and the performance parameter,select a second regression formula, input the linear dependent reasonindicator and performance parameter as independent variables in thesecond regression formula, and generate the at least one KPI as lineardependent variables of the second regression formula.
 17. The HANA ofclaim 10, wherein the cost associated with each of the one or more datapairs is determined based on a cost per unit of an activity associatedwith the reason indicator.